Local Feedback Control Research Articles

A centralized EMPC scheme for PV-powered alkaline electrolyzer

Photovoltaic (PV)-powered alkaline electrolyzer system (PVPAES) is an advanced technique to convert the off-grid and intermittent PV-based solar energy into storable and transportable electrolyzer-based hydrogen energy with zero carbon emissions. However, it is difficult to realize the coordinated control of the off-grid PV module and the alkaline electrolyzer, due to the multiple timescale dynamics. To address this challenge, the PVPAES is decomposed into the slow part and fast part based on the dynamic time scale. Exploiting the decomposed subsystems, the slow one is assumed to be managed well by the auxiliary controller. For the fast one, a centralized economic model predictive control (CEMPC) scheme is constituted. This CEMPC integrates the energy management system and local feedback control into a single optimal control framework. A mathematical model of the PVPAES is established, on the basis of which the CEMPC directly adopts the economic indices as the cost function to realize the flexible power point tracking, power supply-demand balance, and dynamic economic optimization. Moreover, the inherent strong nonlinearity of PVPAES results in the nonconvex mixed-integer nonlinear programming optimization problem in the CEMPC. The exhaustive search algorithm utilizing finite converter switching states is adopted to achieve the global economic optimum. The effectiveness of the proposed CEMPC controller is illustrated through simulations under varying irradiance conditions.

A new control method for leader–follower consensus problem of uncertain constrained nonlinear multi-agent systems

The design of Adaptive Fuzzy Sliding-Mode Control (AFSMC) is discussed in this study for the distributed leader–follower consensus problem of nonlinear uncertain multi-agent systems with input and state constraints. Avoiding the critical circumstances related to the violation of input and state constraints is an important issue in the design of consensus control systems. The limitations of existing adaptive consensus controllers developed for full-state constrained systems motivated the present novel method without restrictive structural assumptions. To convert the original problem to an unconstrained consensus control problem, the proposed control system uses a barrier function-based state transformation method and the input-state linearization methodology. As a result, each agent’s adaptive fuzzy sliding-mode control input, which consists of a fuzzy system and a robust term, is constructed based on the derived representation of the system dynamics. The fuzzy system is used to approximate the dynamic equations of the agent, its neighbors, and maybe the leader in each local feedback control law structure, and the robust term is designed to compensate for the fuzzy approximation error. The magnitudes of the robust terms and the output vectors of the fuzzy systems are also determined using the proposed adaptation laws. The second Lyapunov theorem and Barbalat’s lemma are used to verify the closed-loop system’s asymptotic stability. The effectuality of the proposed methodology is confirmed by numerical simulations for several Autonomous Underwater Vehicles (AUVs). Simulation results show that by means of the proposed AFSMC, the leader–follower consensus is achieved without state constraint violation and performance degradation.

Accurate voltage regulation for a DC microgrid using nonlinear state feedback controller

AbstractThe high penetration of distributed generation systems poses challenges in effectively managing both DC bus voltage and power‐sharing in DC microgrids (MGs). To ensure precise voltage regulation across various distributed generation systems and maintain overall system stability, this paper studies the modelling and control of an islanded DC MG described by a set of nonlinear‐nonaffine differential equations. In the proposed DC MG, a battery storage, a photovoltaic (PV) power plant, and a back‐end power converter with DC loads have been connected to a common DC bus. First, a dynamic model of a DC MG was developed. Then, to find the maximum power point of solar PV, a straightforward method based on an artificial neural net is employed. Afterward, a nonlinear local state feedback controller through output regulation theory is applied to achieve voltage stability, overcoming the drawbacks of droop‐based techniques. Moreover, partial state feedback capability simplifies controller design. To verify the effectiveness of the proposed approach, various scenarios, including sudden load variation, illumination change, and changes in the MG configuration, have been examined. Finally, numerical studies were carried out in a simulation environment to demonstrate the superiority of the proposed nonlinear control scheme over constant feedback control.

Observer-based consensus of multi-agent systems under odd distributed impulsive control protocol

In this paper, for leader-follower Lipschitz nonlinear multi-agent systems (MASs) affected by external disturbances, observer-based distributed controller design problems are discussed in order to realize the MASs consensus. Firstly, to deal with unmeasured system states and the unknown disturbances, for each follower, a joint unknown input observer is proposed. To do so, through an interval observer, a mathematical relationship between the state and the disturbance is set up in advance. Secondarily, by using the estimations of the state and the disturbance, a local feedback controller is designed aiming at stabilizing the linear part of each follower. Thirdly, an odd distributed impulsive control method is proposed which is utilized to deal with the nonlinearity. Then the MASs consensus goal is realized by combining the local feedback controller and the distributed impulsive controller. At last, a simulation is discussed so as to display the effectivity of the raised scheme.

Local dynamic output feedback control of saturated discrete-time T-S fuzzy systems with partially measured premise variables

This paper aims to investigate the problem of designing locally stabilizing dynamic output feedback controllers and estimate the domain of attraction for discrete-time Takagi-Sugeno (T-S) fuzzy systems. A realistic scenario is assumed where the control signal is subject to saturation, and the premise variables are partially or completely unmeasured, that is, not available for the control law. As a result, the fuzzy output controller can have a different number of fuzzy rules and a different set of membership functions from the T-S model. To obtain locally stabilizable conditions, we propose modeling the variation rate of the membership functions without using upper bounds, a new contribution in the context of output control of discrete-time T-S systems. The design conditions are expressed as linear matrix inequality relaxations based on fuzzy Lyapunov functions using slack variables introduced by Finsler's lemma. Numerical examples illustrate the effectiveness of the approach.

Simulation-based assessment of ASHRAE Guideline 36, considering energy performance, indoor air quality, and control stability

This study assesses American Society of Heating, Refrigerating and Air-Conditioning Engineers Guideline 36 (G36) with a typical medium office building. Specifically, this study employed a Modelica model of a variable air volume (VAV) system that serves this building, which includes components for representing indoor virus transmission and filtration. It then implemented the G36 control sequences for both water-side and air-side equipment in Python. After that, this study conducted the assessment by co-simulating G36 and the Modelica model using the Building Operations Testing Framework. Unlike existing works, this work has three unique features: (1) It considers the interactions between control sequences for water-side and air-side equipment of the studied VAV system. (2) It assesses the performance of G36 from the perspective of IAQ. (3) It examines the short-term behaviors of the studied building under G36 to understand the control stability. This assessment confirms significant energy savings from G36, largely because of the interaction between supply air temperature and hot water controls. It also reveals a trade-off between the ability to slow the spread of virus and the energy performance via demand controlled ventilation. Lastly, it emphasizes the necessity of tuning local feedback control when implementing G36.

Field test of Model Predictive Control in residential buildings for utility cost savings

Air conditioners in residential buildings often rely on local feedback control to regulate the indoor temperature, where the unit is cycled on and off by comparing the actual temperature with the temperature set-point. Model Predictive Control (MPC) is a technique that makes use of building models, disturbance forecasts, and optimization algorithms to enhance building performance. MPC has demonstrated its potential in reducing building energy consumption and operating costs; however, it has seldom been applied in residential buildings because of the high implementation costs, complicated hardware configuration, and insufficient data. In this study, we developed and tested a MPC controller in a real apartment aimed at reducing the operating costs based on the local Time-of-Use (ToU) utility structure. We developed a feasible and easy-to-use hardware configuration as well as a cloud-based software deployment. The MPC controllers were implemented and evaluated through one-month field testing in a bedroom and living room of an apartment in a residential building. The field test showed that the MPC controller saved 22.1% for the bedroom and 26.8% cooling costs for the living room, compared to conventional rule-based control (RBC). We further confirmed the robustness of the MPC controller as the field test showed that the MPC controller is able to outperform the conventional RBC controller, even if the model is not accurate. Our work provides a practical MPC solution that can unlock the load flexibility potential of residential buildings, which creates a win–win solution for the households and the grid: saving utility costs for households and helping the grid to manage the balance between supply and demand.

Control of Autonomous Vehicles Via Multistage Linear Feedback Design

AbstractIn this paper, we show how autonomous vehicles performing a certain task can be controlled from one command center in order to achieve certain goals. The command center provides individual feedback gains to each vehicle in the form of one compounded feedback gain that in fact carries feedback gain information for each individual vehicle. Vehicles may be of different nature: satellites, cars, tanks, drowns, robots, unmanned aircraft, underwater vehicles, and ships. It is assumed that their dynamic is weakly coupled, namely, dynamics of each individual vehicle is weakly coupled to dynamics of all other vehicles and that they have the same control input. The weak coupling among the vehicles is represented either by physical interactions among vehicles or coordinating or communication signals. The results obtained are applicable to vehicles having different mathematical linear/linearized state space models. In the case when no interaction/coordination/communication among vehicles is present, and all feedback signals come from the command center (central controller) controller, the vehicle mathematical models can be identical. An important feature of the presented results is that different local feedback controllers can be facilitated for different vehicles using a unique global feedback controller signal designed by the command center. In this paper, we will consider cases of two and three vehicles, but the presented multistage feedback design methodology can be potentially extended to N vehicles. The presentation is done in continuous time. Similar derivations hold in the discrete-time domain.

Proportional Integral Predictive Control of High-Order Fully Actuated Networked Multiagent Systems With Communication Delays

This research is devoted to the cooperative control of high-order fully actuated networked multiagent systems (HOFA-NMASs) with communication delays between network node and sensors, and network node and actuators. A proportional integral (PI) predictive control scheme is developed to effectively implement the cooperation among HOFA-NMASs and actively compensate the communication delays. In this scheme, a local PI feedback controller is applied to stabilize the closed-loop nominal HOFA-NMASs, then a Diophantine equation is presented to establish an incremental HOFA prediction model rather than a reduced-order one, so that the optimization of cost function considered cooperative performance and the compensation of communication delays are completed via multistep output predictions, thus the optimal cooperative controllers are constructed. The further discussion provides a simple condition of simultaneous stability and output consensus of closed-loop HOFA-NMASs, which is easy to verify and extend in practice and is independent on communication delays. An experiment of formation control of air-bearing simulators is shown to prove the availability of the proposed PI predictive control scheme.

Data-driven set-point control for nonlinear nonaffine systems

Optimal set-point control is important in maintaining good control performance of practical industrial processes. Considering the challenges when modeling a complex process, this work presents a data-driven optimal set-point control (DDOSC) scheme for nonlinear nonaffine systems bypassing modeling steps. The outer loop of the proposed DDOSC adopts an ideal nonlinear set-point control function which is theoretically achievable to obtain a perfect tracking. Then, a dynamic linearization (DL) is used to transfer the ideal nonlinear set-point control law into a linear parametric one such that it is implementable through a parameter updating law. To address the nonlinear nonaffine system within a data-driven framework, the DL method is again used to obtain its linear data model which is further updated by a parameter-adaptive law. With a proportional feedback controller in the inner loop, the convergence of the DDOSC method is shown. In addition, the results are further extended by considering a proportional-integral-derivative feedback controller in the inner loop. The simulation results on a numerical example and a car suspension system verify the effectiveness of the proposed DDOSC in improving the performance of the local feedback controller.

Model-Based Reinforcement Learning via Stochastic Hybrid Models

Optimal control of general nonlinear systems is a central challenge in automation. Enabled by powerful function approximators, data-driven approaches to control have recently successfully tackled challenging applications. However, such methods often obscure the structure of dynamics and control behind black-box over-parameterized representations, thus limiting our ability to understand closed-loop behavior. This article adopts a hybrid-system view of nonlinear modeling and control that lends an explicit hierarchical structure to the problem and breaks down complex dynamics into simpler localized units. We consider a sequence modeling paradigm that captures the temporal structure of the data and derive an expectation-maximization (EM) algorithm that automatically decomposes nonlinear dynamics into stochastic piecewise affine models with nonlinear transition boundaries. Furthermore, we show that these time-series models naturally admit a closed-loop extension that we use to extract local polynomial feedback controllers from nonlinear experts via behavioral cloning. Finally, we introduce a novel hybrid relative entropy policy search (Hb-REPS) technique that incorporates the hierarchical nature of hybrid models and optimizes a set of time-invariant piecewise feedback controllers derived from a piecewise polynomial approximation of a global state-value function.

Distributed multi-agent collision avoidance using robust differential game

This paper proposes a novel robust differential game scheme to solve the collision avoidance problem for networked multi-agent systems (MASs), subject to linear dynamics, external disturbances and limited observation capabilities. Compared with the existing differential game approaches only considering obstacle avoidance objectives, we explicitly incorporate the trajectory optimization target by penalizing the deviation from reference trajectories, based on the artificial potential field (APF) concept. It is proved that the strategies of each agent defined by individual optimization problems will converge to a local robust Nash equilibrium (R-NE), which further, with a fixed strong connection topology, will converge to the global R-NE. Additionally, to cope with the limited observation for MASs, local robust feedback control strategies are constructed based on the best approximate cost function and distributed robust Hamilton–Jacobi–Isaacs (DR-HJI) equations, which does not require global information of agents as in the traditional Riccati equation form. The feedback gains of the control strategies are found via the ant colony optimization (ACO) algorithm with a non-dominant sorting structure with convergence guarantees. Finally, simulation results are provided to verify the efficacy and robustness of the novel scheme. The agents arrived at the targeted position collision-free with a reduced arrival time, and reached the targeted positions under disturbance.

Distributed containment control of networked uncertain MIMO pure‐feedback nonlinear systems via quantized state feedback and communication

AbstractThis article presents a distributed adaptive containment control strategy using quantized state feedback and communication for uncertain multiple‐input‐multiple‐output pure‐feedback nonlinear multi‐agent systems with state quantization. It is assumed that quantized state variables of multiple followers and quantized outputs of multiple dynamic leaders are only available for constructing local adaptive feedback controllers of followers under a directed network. Compared with existing containment control approaches, the primary contribution of this study is dealing with discontinuously quantized state feedback and communication problems for containment control design in the presence of unknown nonaffine nonlinearities. Based on quantized state and communication information, local adaptive neural network trackers of followers and their adaptive tuning laws are developed to guarantee that the trajectories of all followers converge to the dynamic convex hull spanned by the multiple leaders regardless of unknown pure‐feedback nonlinearities. By analyzing the stability of the local quantization errors between quantized signals and original signals, we prove the boundedness of all closed‐loop signals and the convergence of the containment errors to a sufficiently small domain around the origin. Simulation examples involving flexible‐joint manipulators are presented to validate the proposed quantized feedback scheme.

Hierarchical Cooperative Control of Connected Vehicles: From Heterogeneous Parameters to Heterogeneous Structures

As one of the typical applications of connected vehicles (CVs), the vehicle platoon control technique has been proven to have the advantages of reducing emissions, improving traffic throughout and driving safety. In this paper, a unified hierarchical framework is designed for cooperative control of CVs with both heterogeneous model parameters and structures. By separating neighboring information interaction from local dynamics control, the proposed framework is designed to contain an upper-level observing layer and a lower-level tracking control layer, which helps address the heterogeneity in vehicle parameters and structures. Within the proposed framework, an observer is designed for following vehicles to observe the leading vehicle's states using neighboring communication, while a tracking controller is designed to track the observed leading vehicle using local feedback control. Closed-loop stability in the absence and presence of communication time delay is analyzed, and the observer is further extended to a finite time convergent one to address string stability under general communication topology. Numerical simulation and field experiment verify the effectiveness of the proposed method.

Prevalence and scalable control of localized networks

The ability to control network dynamics is essential for ensuring desirable functionality of many technological, biological, and social systems. Such systems often consist of a large number of network elements, and controlling large-scale networks remains challenging because the computation and communication requirements increase prohibitively fast with network size. Here, we introduce a notion of network locality that can be exploited to make the control of networks scalable, even when the dynamics are nonlinear. We show that network locality is captured by an information metric and is almost universally observed across real and model networks. In localized networks, the optimal control actions and system responses are both shown to be necessarily concentrated in small neighborhoods induced by the information metric. This allows us to develop localized algorithms for determining network controllability and optimizing the placement of driver nodes. This also allows us to develop a localized algorithm for designing local feedback controllers that approach the performance of the corresponding best global controllers, while incurring a computational cost orders-of-magnitude lower. We validate the locality, performance, and efficiency of the algorithms in Kuramoto oscillator networks, as well as three large empirical networks: synchronization dynamics in the Eastern US power grid, epidemic spreading mediated by the global air-transportation network, and Alzheimer's disease dynamics in a human brain network. Taken together, our results establish that large networks can be controlled with computation and communication costs comparable to those for small networks.

Wind-Induced Response Control of a Television Transmission Tower by Piezoelectric Semi-Active Friction Dampers

To be a typical flexible structure with small damping, a high-rise television (TV) transmission tower is sensitive to wind excitations. The response control of TV transmission towers by semi-active friction dampers under strong external excitations is still very limited and the parametric study on control performance has not been systematically investigated. To this end, the semi-active control of a wind-excited TV transmission tower by piezoelectric friction dampers is conducted in this study. A fine finite element (FE) model of a real TV transmission tower is constructed and then a two-dimensional (2D) dynamic model is also established. The analytical model of a semi-active friction damper based on piezoelectric actuators is then established with damper stiffness considered. The equations of motion of the tower with friction dampers are then established. The local feedback control algorithm based on nonlinear Reid damping mechanisms is used to command piezoelectric friction dampers for wind-excited towers. A high-rise TV transmission tower in China is used to investigate the validity of the proposed semi-active control approach and compared with that of passive control. Furthermore, a detailed parametric investigation is conducted to examine the influence of gain coefficient, damper stiffness, hysteresis loops, damper force, and wind loading intensity on control efficacy. The analytical results indicate that piezoelectric friction dampers with optimal parameters are beneficial to the vibration mitigation of TV transmission towers under different wind loading intensities.

Finite‐time local piecewise control for parabolic PDEs with ODE output feedback

This paper is devoted to the finite-time local piecewise control for parabolic partial differential equations (PDEs) by using dynamic output feedback control strategy, where the controller is designed as an ordinary differential equation (ODE). This makes the closed-loop system PDE-ODE coupled, which is employed to accurately describe the dynamics of the PDE system. According to the constructed PDE-ODE coupled model, a local piecewise dynamic feedback control law is first proposed. Sufficient conditions on finite-time stabilisation of the parabolic PDE-ODE coupled system by the suggested feedback controller are then developed in the sense of both complete spatial measurement and incomplete spatial measurement of the observed output of the PDE system, respectively. Finally, the issues regarding the finite-time stabilisation of the closed-loop system is converted into the feasibility of matrix inequalities, and some simulation studies are provided to verify the effectiveness of the proposed results.

Design of robust hierarchical control for homogeneous linear multi-agent systems with parametric uncertainty and external disturbance

This paper presents the design of robust hierarchical control for homogeneous linear multi-agent systems (MAS) subject to parametric uncertainty and external disturbance. Specifically, the control design is based on a two-layer hierarchical structure consisting of an upper layer and a lower layer. In the lower layer, each agent is represented by a linear time-invariant system and executes a local action. Moreover, each agent exchanges information with neighbouring agents in the upper layer through an undirected graph to achieve the global goal of stabilisation and disturbance attenuation for the MAS. We propose two robust control designs, namely, robust hierarchical control and robust hierarchical control. The design of local and global feedback control laws is formulated as a constrained optimisation over linear matrix inequalities (LMI). The LMI formulation can effectively incorporate the design objective to minimise disturbance attenuation. Numerical results show that the proposed robust controls ensure robust stability of MAS and outperform the existing nominal controls by improving the disturbance attenuation.

Coherent self-control of free-space optical beams with integrated silicon photonic meshes

In technologies operating at light wavelengths for wireless communication, sensor networks, positioning, and ranging, a dynamic coherent control and manipulation of light fields is an enabling element for properly generating and correctly receiving free-space optical (FSO) beams even in the presence of unpredictable objects and turbulence in the light path. In this work, we use a programmable mesh of Mach–Zehnder (MZI) interferometers to automatically control the complex field radiated and captured by an array of optical antennas. The implementation of local feedback control loops in each MZI stage, without global multivariable optimization techniques, enables an unlimited scalability. Several functionalities are demonstrated, including the generation of perfectly shaped beams with nonperfect optical antennas, the imaging of a desired field pattern through an obstacle or a diffusive medium, and the identification of an unknown obstacle inserted in the FSO path. Compared to conventional devices used for the manipulation of FSO beams, such as spatial light modulators, our programmable device can self-configure through automated control strategies and can be integrated with other functionalities implemented onto the same photonic chip.

Functional architecture of neural circuits for leg proprioception in Drosophila

SUMMARYTo effectively control their bodies, animals rely on feedback from proprioceptive mechanosensory neurons. In the Drosophila leg, different proprioceptor subtypes monitor joint position, movement direction, and vibration. Here, we investigate how these diverse sensory signals are integrated by central proprioceptive circuits. We find that signals for leg joint position and directional movement converge in second-order neurons, revealing pathways for local feedback control of leg posture. Distinct populations of second-order neurons integrate tibia vibration signals across pairs of legs, suggesting a role in detecting external substrate vibration. In each pathway, the flow of sensory information is dynamically gated and sculpted by inhibition. Overall, our results reveal parallel pathways for processing of internal and external mechanosensory signals, which we propose mediate feedback control of leg movement and vibration sensing, respectively. The existence of a functional connectivity map also provides a resource for interpreting connectomic reconstruction of neural circuits for leg proprioception.